Results: The quantitative comparisons between our proposed method and the others are presented in Tab. REF  and Tab. REF . The first interesting observation is that the widely-used CRF Loss [1]} achieves the worst performance than all other methods. The reason may be the CRF Loss [1]} is specifically designed for natural image segmentation tasks and is not suitable to handle the CT images with low contrast and non-enhancement. Then, we found that the network is capable of leveraging the scribble annotation more efficiently by encouraging to produce more confident predictions. Moreover, compared with the entropy minimization term, our proposed intra-class intensity variance minimization achieves better results, the mean \(DSC\)  of 73.90% \(vs\)  74.99%. In addition, combining the entropy minimization and intra-class intensity variance minimization, the model achieves the best performance than the others and improves the mean \(DSC\)  from 72.06% to 76.81%. These results demonstrate that also most weakly supervised methods achieve better results than using partially cross-entropy loss, except the CRF Loss. It is noteworthy to mention that the scribble annotations save more than 96% of label costs than dense annotations. Finally, we find large size organs weakly supervised segmentation results are very close to fully supervised, especially in the liver, spleen, kidney, stomach, head of the femur. However, the small size organs still can not be segmented well, it also points out the research direction going. It also shows that with further research, weakly-supervised learning may further reduce the label costs in the future.
